臺灣博碩士論文加值系統

文件分群係根據一群文件的內容自動將其組織成有意義的類別。現有的文件分群技術大多只處理單語文件，也就是所有文件只以單一種語言所寫成。然而隨著國際化的趨勢以及網際網路科技的發展，組織或個人常常會產生獲取進而儲存不同語言之文件，也因此產生了對多語文件自動分群技術的需要。此技術的重要性及需要性激發了本研究的動機，於是我們設計了一個以字詞翻譯為基礎之多語文件自動分群技術。我們的實證評估以 cluster recall 與cluster precision 來衡量分群效果，結果顯示所提出之多語文件分群技術達到令人滿意的效果。

Document clustering automatically organizes a document collection into distinct groups of similar documents on the basis of their contents. Most of existing document clustering techniques deal with monolingual documents (i.e., documents written in one language). However, with the trend of globalization and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for multilingual document clustering (MLDC). Motivated by its significance and need, this study designs a translation-based MLDC technique. Our empirical evaluation results show that the proposed multilingual document clustering technique achieves satisfactory clustering effectiveness measured by both cluster recall and cluster precision.

Chapter 1 Introduction.............................................................................1
1.1 Background .....................................................................................................1
1.2 Research Motivation and Objectives .............................................................2
1.3 Organization of the Thesis .............................................................................5
Chapter 2 Literature Review...................................................................6
2.1 Monolingual Document Clustering Technique .............................................6
2.2 Multilingual Document Clustering (MLDC) Technique ............................10
Chapter 3 Design of Multilingual Thesaurus Translation-based
MLDC Technique....................................................................................14
3.1 Multilingual Thesaurus Construction..........................................................15
3.2 Document Translation ..................................................................................18
3.3 Document Clustering....................................................................................20
Chapter 4 Empirical Evaluation ...........................................................23
4.1 Data Collection .............................................................................................23
4.2 Evaluation Criteria .......................................................................................24
4.3 Evaluation Procedure ...................................................................................25
4.4 Evaluation Benchmark .................................................................................26
4.5 Tuning Experiments and Results .................................................................27
4.5.1 Tuning Number of Features k ...........................................................27
4.5.2 Tuning Translation Threshold δaw ....................................................31
4.6 Comparative Evaluation Results .................................................................35
Chapter 5 Conclusion and Future Research Directions......................40
References ................................................................................................42

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